• Energy Research

Abstract Nitrogen expander-based natural gas (NG) liquefaction processes are considered to be the most feasible and economic practices for offshore applications. The nitrogen single expander process is simple owing to its single-phase operation, and it employs high occupational safety and environment-friendly refrigerants. However, this process has low energy efficiency. Recent advancements in expansion devices show the possibility of handling two-phase refrigerant flow in an economical and simple manner. Accordingly, this paper proposes two-phase expansion using an innovative binary mixed refrigerant (MR) composed of ethane and nitrogen (C2N). Furthermore, a propane-precooling refrigeration cycle is also implemented and evaluated to analyze greater potential benefits of ethane–nitrogen refrigerant with two-phase expansion. To assess the technical and commercial feasibility of the proposed liquefaction process, energy, exergy, and economic (3E) analysis is performed. Using the C2N two-phase expander LNG process, 47.83% energy can be saved with 55.25% exergy destruction minimization and 24.12% total annualized costs (TAC) savings as compared to previously published nitrogen single expander process. Whereas, the C3-precooled C2N process gives higher energy savings i.e., 52.45% but low TAC savings i.e., 1.6% as compared to nitrogen single expander LNG process. Considering TAC savings, the C2N process (without propane-precooling) can be a promising candidate for offshore applications.

Abstract Nitrogen expander-based natural gas (NG) liquefaction processes are considered to be the most feasible and economic practices for offshore applications. The nitrogen single expander process is simple owing to its single-phase operation, and it employs high occupational safety and environment-friendly refrigerants. However, this process has low energy efficiency. Recent advancements in expansion devices show the possibility of handling two-phase refrigerant flow in an economical and simple manner. Accordingly, this paper proposes two-phase expansion using an innovative binary mixed refrigerant (MR) composed of ethane and nitrogen (C2N). Furthermore, a propane-precooling refrigeration cycle is also implemented and evaluated to analyze greater potential benefits of ethane–nitrogen refrigerant with two-phase expansion. To assess the technical and commercial feasibility of the proposed liquefaction process, energy, exergy, and economic (3E) analysis is performed. Using the C2N two-phase expander LNG process, 47.83% energy can be saved with 55.25% exergy destruction minimization and 24.12% total annualized costs (TAC) savings as compared to previously published nitrogen single expander process. Whereas, the C3-precooled C2N process gives higher energy savings i.e., 52.45% but low TAC savings i.e., 1.6% as compared to nitrogen single expander LNG process. Considering TAC savings, the C2N process (without propane-precooling) can be a promising candidate for offshore applications.

Abstract This study unfolds the advanced process configuration modification in the evolution of a dual mixed refrigerant (DMR) process for natural gas liquefaction, followed by its optimization through a unique approach i.e., teaching–learning self-study optimization (TLSO). The DMR process is improved by replacing Joule Thomson valves with the isentropic cryogenic turbines. To ensure the maximum possible thermodynamic performance of the retrofitted DMR process, the TLSO paradigm is used and evaluated. The energy, exergy, coefficient of performance, and figure of merit are determined and compared with conventional bench-scale DMR process to find the performance improvement opportunities in the proposed cryogenic turbine-retrofitted DMR process. The performance analysis revealed that the proposed optimal retrofitted DMR process could produce LNG using 28.57% less energy than the base case. The detailed thermodynamic evaluation revealed that the proposed DMR process has 64.68% exergy efficiency, 2.42 coefficient of performance, and 41.6% figure of merit, which are 13.37%, 19%, and 11.9%, higher than the conventional DMR process, respectively. This study would significantly help process engineers overcome the challenges of relating energy efficiency of the LNG plants for both onshore and offshore applications.

Abstract This study unfolds the advanced process configuration modification in the evolution of a dual mixed refrigerant (DMR) process for natural gas liquefaction, followed by its optimization through a unique approach i.e., teaching–learning self-study optimization (TLSO). The DMR process is improved by replacing Joule Thomson valves with the isentropic cryogenic turbines. To ensure the maximum possible thermodynamic performance of the retrofitted DMR process, the TLSO paradigm is used and evaluated. The energy, exergy, coefficient of performance, and figure of merit are determined and compared with conventional bench-scale DMR process to find the performance improvement opportunities in the proposed cryogenic turbine-retrofitted DMR process. The performance analysis revealed that the proposed optimal retrofitted DMR process could produce LNG using 28.57% less energy than the base case. The detailed thermodynamic evaluation revealed that the proposed DMR process has 64.68% exergy efficiency, 2.42 coefficient of performance, and 41.6% figure of merit, which are 13.37%, 19%, and 11.9%, higher than the conventional DMR process, respectively. This study would significantly help process engineers overcome the challenges of relating energy efficiency of the LNG plants for both onshore and offshore applications.

Coal-fired power plants have been used to meet the energy requirements in countries where coal reserves are abundant and are the key source of NOx emissions. Owing to the serious environmental and health concerns associated with NOx emissions, much work has been carried out to reduce NOx emissions. Sophisticated artificial intelligence (AI) techniques have been employed during the past few decades, such as least-squares support vector machine (LSSVM), artificial neural networks (ANN), long short-term memory (LSTM), and gated recurrent unit (GRU), to develop the NOx prediction model. Several studies have investigated deep neural networks (DNN) models for accurate NOx emission prediction. However, there is a need to investigate a DNN-based NOx prediction model that is accurate and computationally inexpensive. Recently, a new AI technique, convolutional neural network (CNN), has been introduced and proven superior for image class prediction accuracy. According to the best of the author’s knowledge, not much work has been done on the utilization of CNN on NOx emissions from coal-fired power plants. Therefore, this study investigated the prediction performance and computational time of one-dimensional CNN (1D-CNN) on NOx emissions data from a 500 MW coal-fired power plant. The variations of hyperparameters of LSTM, GRU, and 1D-CNN were investigated, and the performance metrics such as RMSE and computational time were recorded to obtain optimal hyperparameters. The obtained optimal values of hyperparameters of LSTM, GRU, and 1D-CNN were then employed for models’ development, and consequently, the models were tested on test data. The 1D-CNN NOx emission model improved the training efficiency in terms of RMSE by 70.6% and 60.1% compared to LSTM and GRU, respectively. Furthermore, the testing efficiency for 1D-CNN improved by 10.2% and 15.7% compared to LSTM and GRU, respectively. Moreover, 1D-CNN (26 s) reduced the training time by 83.8% and 50% compared to LSTM (160 s) and GRU (52 s), respectively. Results reveal that 1D-CNN is more accurate, more stable, and computationally inexpensive compared to LSTM and GRU on NOx emission data from the 500 MW power plant.

Coal-fired power plants have been used to meet the energy requirements in countries where coal reserves are abundant and are the key source of NOx emissions. Owing to the serious environmental and health concerns associated with NOx emissions, much work has been carried out to reduce NOx emissions. Sophisticated artificial intelligence (AI) techniques have been employed during the past few decades, such as least-squares support vector machine (LSSVM), artificial neural networks (ANN), long short-term memory (LSTM), and gated recurrent unit (GRU), to develop the NOx prediction model. Several studies have investigated deep neural networks (DNN) models for accurate NOx emission prediction. However, there is a need to investigate a DNN-based NOx prediction model that is accurate and computationally inexpensive. Recently, a new AI technique, convolutional neural network (CNN), has been introduced and proven superior for image class prediction accuracy. According to the best of the author’s knowledge, not much work has been done on the utilization of CNN on NOx emissions from coal-fired power plants. Therefore, this study investigated the prediction performance and computational time of one-dimensional CNN (1D-CNN) on NOx emissions data from a 500 MW coal-fired power plant. The variations of hyperparameters of LSTM, GRU, and 1D-CNN were investigated, and the performance metrics such as RMSE and computational time were recorded to obtain optimal hyperparameters. The obtained optimal values of hyperparameters of LSTM, GRU, and 1D-CNN were then employed for models’ development, and consequently, the models were tested on test data. The 1D-CNN NOx emission model improved the training efficiency in terms of RMSE by 70.6% and 60.1% compared to LSTM and GRU, respectively. Furthermore, the testing efficiency for 1D-CNN improved by 10.2% and 15.7% compared to LSTM and GRU, respectively. Moreover, 1D-CNN (26 s) reduced the training time by 83.8% and 50% compared to LSTM (160 s) and GRU (52 s), respectively. Results reveal that 1D-CNN is more accurate, more stable, and computationally inexpensive compared to LSTM and GRU on NOx emission data from the 500 MW power plant.

The synthesis of biodiesel from Jatropha curcas by transesterification is kinetically controlled. It depends on the molar ratio, reaction time, and temperature, as well as the catalyst nature and quantity. The aim of this study was to explore the transesterification of low-cost, inedible J. curcas seed oil utilizing both homogenous (potassium hydroxide; KOH) and heterogenous (calcium oxide; CaO) catalysis. In this effort, two steps were used. First, free fatty acids in J. curcas oil were reduced from 12.4 to less than 1 wt.% with sulfuric acid-catalyzed pretreatment. Transesterification subsequently converted the oil to biodiesel. The yield of fatty acid methyl esters was optimized by varying the reaction time, catalyst load, and methanol-to-oil molar ratio. A maximum yield of 96% was obtained from CaO nanoparticles at a reaction time of 5.5 h with 4 wt.% of the catalyst and an 18:1 methanol-to-oil molar ratio. The optimum conditions for KOH were a molar ratio of methanol to oil of 9:1, 5 wt.% of the catalyst, and a reaction time of 3.5 h, and this returned a yield of 92%. The fuel properties of the optimized biodiesel were within the limits specified in ASTM D6751, the American biodiesel standard. In addition, the 5% blends in petroleum diesel were within the ranges prescribed in ASTM D975, the American diesel fuel standard.